OpenRaiser/Intern-Atlas huggingface dataset:
AoCStream: All-on-Chip CNN Accelerator With Stream-Based Line-Buffer Architecture
Venue: arXiv cs.AR 2022
Year: 2022

Convolutional neural network (CNN) accelerators are being widely used for their efficiency, but they require a large amount of memory, leading to the use of a slow and power consuming external memory. This paper exploits two schemes to reduce the required memory amount and ultimately to implement a CNN of reasonable performance only with on-chip memory of a practical device like a low-end FPGA. To reduce the memory amount of the intermediate data, a stream-based line-buffer architecture and a dataflow for the architecture are proposed instead of the conventional frame-based architecture, where the amount of the intermediate data memory is proportional to the square of the input image size. The architecture consists of layer-dedicated blocks operating in a pipelined way with the input and output streams. Each convolutional layer block has a line buffer storing just a few rows of input data. The sizes of the line buffers are proportional to the width of the input image, so the architecture requires less intermediate data storage than the conventional frame-based architecture, especially in the trend of getting larger input size in modern object detection CNNs. In addition to the reduced intermediate data storage, the weight memory is reduced by the accelerator-aware pruning. The experimental results show that a whole object detection CNN can be implemented even on a low-end FPGA without an external memory. Compared to previous accelerators with similar object detection accuracy, the proposed accelerator reaches much higher throughput even with less FPGA resources of LUTs, registers, and DSPs, showing much higher efficiency. The trained models and implemented bit files are available at https://github.com/HyeongjuKang/accelerator-aware-pruning and https://github.com/HyeongjuKang/aocstream.
Understanding Postpartum Parents' Experiences via Two Digital Platforms
Venue: arXiv cs.CL 2022
Year: 2022

Digital platforms, including online forums and helplines, have emerged as avenues of support for caregivers suffering from postpartum mental health distress. Understanding support seekers' experiences as shared on these platforms could provide crucial insight into caregivers' needs during this vulnerable time. In the current work, we provide a descriptive analysis of the concerns, psychological states, and motivations shared by healthy and distressed postpartum support seekers on two digital platforms, a one-on-one digital helpline and a publicly available online forum. Using a combination of human annotations, dictionary models and unsupervised techniques, we find stark differences between the experiences of distressed and healthy mothers. Distressed mothers described interpersonal problems and a lack of support, with 8.60% - 14.56% reporting severe symptoms including suicidal ideation. In contrast, the majority of healthy mothers described childcare issues, such as questions about breastfeeding or sleeping, and reported no severe mental health concerns. Across the two digital platforms, we found that distressed mothers shared similar content. However, the patterns of speech and affect shared by distressed mothers differed between the helpline vs. the online forum, suggesting the design of these platforms may shape meaningful measures of their support-seeking experiences. Our results provide new insight into the experiences of caregivers suffering from postpartum mental health distress. We conclude by discussing methodological considerations for understanding content shared by support seekers and design considerations for the next generation of support tools for postpartum parents.
Vehicle in Virtual Environment (VVE) Method
Venue: arXiv cs.RO 2022
Year: 2022

Autonomous vehicle (AV) algorithms need to be tested extensively in order to make sure the vehicle and the passengers will be safe while using it after the implementation. Testing these algorithms in real world create another important safety critical point. Real world testing is also subjected to limitations such as logistic limitations to carry or drive the vehicle to a certain location. For this purpose, hardware in the loop (HIL) simulations as well as virtual environments such as CARLA and LG SVL are used widely. This paper discusses a method that combines the real vehicle with the virtual world, called vehicle in virtual environment (VVE). This method projects the vehicle location and heading into a virtual world for desired testing, and transfers back the information from sensors in the virtual world to the vehicle. As a result, while vehicle is moving in the real world, it simultaneously moves in the virtual world and obtains the situational awareness via multiple virtual sensors. This would allow testing in a safe environment with the real vehicle while providing some additional benefits on vehicle dynamics fidelity, logistics limitations and passenger experience testing. The paper also demonstrates an example case study where path following and the virtual sensors are utilized to test a radar based stopping algorithm.
Accelerating CNN inference on long vector architectures via co-design
Venue: arXiv cs.DC 2022
Year: 2022

CPU-based inference can be an alternative to off-chip accelerators, and vector architectures are a promising option due to their efficiency. However, the large design space of convolutional algorithms and hardware implementations makes it challenging to select the best options. This paper presents ongoing research into co-designing vector architectures for CPU-based CNN inference, focusing on the im2col+GEMM and Winograd kernels. Using the Gem5 simulator, we examine the impact of various hardware microarchitectural features on RISC-V Vector and ARM-SVE ISAs. We also study the impact of several BLIS-like algorithmic optimizations on im2col+GEMM. Our co-design study shows that longer vector lengths and larger caches can improve performance by 5x with our optimized CNN kernels, compared to a vector length of 512-bit and 1MB of L2 cache. For Winograd, we present a novel approach of inter-tile parallelization that exploits longer vector lengths and offers high memory reuse, resulting in up to 2.4x performance improvement for non-strided convolutional layers with 3x3 kernel size. Our study also shows that Winograd requires smaller cache sizes compared to im2col+GEMM.
Taking off with Biodegradable Tensegrities: An Eco-friendly Emergency Medical Delivery Solution
Venue: arXiv cs.CY 2022
Year: 2022

This paper analyzes how medical supplies can be safely transported through eco-friendly means to areas affected by disasters such as floods using Unmanned Aerial Vehicles. We compared the performance of different bio-degradable tensegrity structures with fragile payloads cushioned with coir padding by conducting drop tests from successively greater heights upto 75 meters on surfaces with different hardness. Our results showed that the bio-degradable tensegrities was able to absorb the shock on impact successively in all cases without any damage to the payload. The results suggest that biodegradable tensegrities are a viable option for fragile payload delivery.
A Graph-Transformational Approach for Proving the Correctness of Reductions between NP-Problems
Venue: arXiv cs.CC 2022
Year: 2022

The complexity class NP of decision problems that can be solved nondeterministically in polynomial time is of great theoretical and practical importance where the notion of polynomial-time reductions between NP-problems is a key concept for the study of NP. As many typical NP-problems are naturally described as graph problems, they and their reductions are obvious candidates to be investigated by graph-transformational means. In this paper, we propose such a graph-transformational approach for proving the correctness of reductions between NP-problems.
Set-Transformer BeamsNet for AUV Velocity Forecasting in Complete DVL Outage Scenarios
Venue: arXiv cs.RO 2022
Year: 2022

Autonomous underwater vehicles (AUVs) are regularly used for deep ocean applications. Commonly, the autonomous navigation task is carried out by a fusion between two sensors: the inertial navigation system and the Doppler velocity log (DVL). The DVL operates by transmitting four acoustic beams to the sea floor, and once reflected back, the AUV velocity vector can be estimated. However, in real-life scenarios, such as an uneven seabed, sea creatures blocking the DVL's view and, roll/pitch maneuvers, the acoustic beams' reflection is resulting in a scenario known as DVL outage. Consequently, a velocity update is not available to bind the inertial solution drift. To cope with such situations, in this paper, we leverage our BeamsNet framework and propose a Set-Transformer-based BeamsNet (ST-BeamsNet) that utilizes inertial data readings and previous DVL velocity measurements to regress the current AUV velocity in case of a complete DVL outage. The proposed approach was evaluated using data from experiments held in the Mediterranean Sea with the Snapir AUV and was compared to a moving average (MA) estimator. Our ST-BeamsNet estimated the AUV velocity vector with an 8.547% speed error, which is 26% better than the MA approach.
DuAT: Dual-Aggregation Transformer Network for Medical Image Segmentation
Venue: arXiv cs.CV 2022
Year: 2022

Transformer-based models have been widely demonstrated to be successful in computer vision tasks by modelling long-range dependencies and capturing global representations. However, they are often dominated by features of large patterns leading to the loss of local details (e.g., boundaries and small objects), which are critical in medical image segmentation. To alleviate this problem, we propose a Dual-Aggregation Transformer Network called DuAT, which is characterized by two innovative designs, namely, the Global-to-Local Spatial Aggregation (GLSA) and Selective Boundary Aggregation (SBA) modules. The GLSA has the ability to aggregate and represent both global and local spatial features, which are beneficial for locating large and small objects, respectively. The SBA module is used to aggregate the boundary characteristic from low-level features and semantic information from high-level features for better preserving boundary details and locating the re-calibration objects. Extensive experiments in six benchmark datasets demonstrate that our proposed model outperforms state-of-the-art methods in the segmentation of skin lesion images, and polyps in colonoscopy images. In addition, our approach is more robust than existing methods in various challenging situations such as small object segmentation and ambiguous object boundaries.
Morpheus: An A-sized AUV with morphing fins and algorithms for agile maneuvering
Venue: arXiv cs.RO 2022
Year: 2022

We designed and constructed an A-sized base autonomous underwater vehicle (AUV), augmented with a stack of modular and extendable hardware and software, including autonomy, navigation, control and high fidelity simulation capabilities (A-size stands for the standard sonobuoy form factor, with a maximum diameter of 124 mm). Subsequently, we extended this base vehicle with a novel tuna-inspired morphing fin payload module (referred to as the Morpheus AUV), to achieve good directional stability and exceptional maneuverability; properties that are highly desirable for rigid hull AUVs, but are presently difficult to achieve because they impose contradictory requirements. The morphing fin payload allows the base AUV to dynamically change its stability-maneuverability qualities by using morphing fins, which can be deployed, deflected and retracted, as needed. The base vehicle and Morpheus AUV were both extensively field tested in-water in the Charles river, Massachusetts, USA; by conducting hundreds of hours of operations over a period of two years. The maneuvering capability of the Morpheus AUV was evaluated with and without the use of morphing fins to quantify the performance improvement. The Morpheus AUV was able to showcase an exceptional turning rate of around 25-35 deg/s. A maximum turn rate improvement of around 35% - 50% was gained through the use of morphing fins.
Some recent advances in reasoning based on analogical proportions
Venue: arXiv cs.AI 2022
Year: 2022

Analogical proportions compare pairs of items (a, b) and (c, d) in terms of their differences and similarities. They play a key role in the formalization of analogical inference. The paper first discusses how to improve analogical inference in terms of accuracy and in terms of computational cost. Then it indicates the potential of analogical proportions for explanation. Finally, it highlights the close relationship between analogical proportions and multi-valued dependencies, which reveals an unsuspected aspect of the former.
A literature review on different types of empirically evaluated bug localization approaches
Venue: arXiv cs.SE 2022
Year: 2022

Today, software systems have a significant role in various domains among which are healthcare, entertainment, transport and logistics, and many more. It is only natural that with this increasing dependency on software, the number of software systems increases. Additionally, these systems become more and more complex. All this leads to a rise in the number of software faults also known as bugs. As a result, the ability to locate the source of a bug (e.g. a file or a commit) is vital for the development and maintenance of efficient software solutions. Bug localization refers to the automated process of discovering files that contain bugs, based on a bug report. This research project aims to make a literature review on different techniques for bug localization. This study distinguishes itself from other surveys and literature reviews [1] in one significant way. The focus of the work is on identifying, categorizing and analyzing existing bug localization methods and tools which were evaluated in an industrial setting. To the best of my knowledge, there are no other works that prioritise this aspect. Unfortunately, such literature is scarce, therefore, bug localization techniques evaluated on open source software are also included.
Fast Exploration of the Impact of Precision Reduction on Spiking Neural Networks
Venue: arXiv cs.NE 2022
Year: 2022

Approximate Computing (AxC) techniques trade off the computation accuracy for performance, energy, and area reduction gains. The trade-off is particularly convenient when the applications are intrinsically tolerant to some accuracy loss, as in the Spiking Neural Networks (SNNs) case. SNNs are a practical choice when the target hardware reaches the edge of computing, but this requires some area minimization strategies. In this work, we employ an Interval Arithmetic (IA) model to develop an exploration methodology that takes advantage of the capability of such a model to propagate the approximation error to detect when the approximation exceeds tolerable limits by the application. Experimental results confirm the capability of reducing the exploration time significantly, providing the chance to reduce the network parameters' size further and with more fine-grained results.
A Mutation-based Text Generation for Adversarial Machine Learning Applications
Venue: arXiv cs.CL 2022
Year: 2022

Many natural language related applications involve text generation, created by humans or machines. While in many of those applications machines support humans, yet in few others, (e.g. adversarial machine learning, social bots and trolls) machines try to impersonate humans. In this scope, we proposed and evaluated several mutation-based text generation approaches. Unlike machine-based generated text, mutation-based generated text needs human text samples as inputs. We showed examples of mutation operators but this work can be extended in many aspects such as proposing new text-based mutation operators based on the nature of the application.
Kokkos-Based Implementation of MPCD on Heterogeneous Nodes
Venue: arXiv cs.DC 2022
Year: 2022

The Kokkos based library Cabana, which has been developed in the Co-design Center for Particle Applications (CoPA), is used for the implementation of Multi-Particle Collision Dynamics (MPCD), a particle-based description of hydrodynamic interactions. It allows a performance portable implementation, which has been used to study the interplay between CPU and GPU usage on a multi-node system. As a result, we see most advantages in a homogeneous GPU usage, but we also discuss the extent to heterogeneous applications, using both CPU and GPU concurrently.
Towards Transportation Digital Twin Systems for Traffic Safety and Mobility Applications: A Review
Venue: arXiv cs.CY 2022
Year: 2022

Digital twin (DT) systems aim to create virtual replicas of physical objects that are updated in real time with their physical counterparts and evolve alongside the physical assets throughout its lifecycle. Transportation systems are poised to significantly benefit from this new paradigm. In particular, DT technology can augment the capabilities of intelligent transportation systems. However, the development and deployment of networkwide transportation DT systems need to take into consideration the scale and dynamic nature of future connected and automated transportation systems. Motivated by the need of understanding the requirements and challenges involved in developing and implementing such systems, this paper proposes a hierarchical concept for a Transportation DT (TDT) system starting from individual transportation assets and building up to the entire networkwide TDT. A reference architecture is proposed for TDT systems that could be used as a guide in developing TDT systems at any scale within the presented hierarchical concept. In addition, several use cases are presented based upon the reference architecture which illustrate the utility of a TDT system from transportation safety, mobility and environmental applications perspective. This is followed by a review of current studies in the domain of TDT systems. Finally, the critical challenges and promising future research directions in TDT are discussed to overcome existing barriers to realize a safe and operationally efficient connected and automated transportation systems.
Exploring the Optimized Value of Each Hyperparameter in Various Gradient Descent Algorithms
Venue: arXiv cs.LG 2022
Year: 2022

In the recent years, various gradient descent algorithms including the methods of gradient descent, gradient descent with momentum, adaptive gradient (AdaGrad), root-mean-square propagation (RMSProp) and adaptive moment estimation (Adam) have been applied to the parameter optimization of several deep learning models with higher accuracies or lower errors. These optimization algorithms may need to set the values of several hyperparameters which include a learning rate, momentum coefficients, etc. Furthermore, the convergence speed and solution accuracy may be influenced by the values of hyperparameters. Therefore, this study proposes an analytical framework to use mathematical models for analyzing the mean error of each objective function based on various gradient descent algorithms. Moreover, the suitable value of each hyperparameter could be determined by minimizing the mean error. The principles of hyperparameter value setting have been generalized based on analysis results for model optimization. The experimental results show that higher efficiency convergences and lower errors can be obtained by the proposed method.
Linear features segmentation from aerial images
Venue: arXiv cs.CV 2022
Year: 2022

The rapid development of remote sensing technologies have gained significant attention due to their ability to accurately localize, classify, and segment objects from aerial images. These technologies are commonly used in unmanned aerial vehicles (UAVs) equipped with high-resolution cameras or sensors to capture data over large areas. This data is useful for various applications, such as monitoring and inspecting cities, towns, and terrains. In this paper, we presented a method for classifying and segmenting city road traffic dashed lines from aerial images using deep learning models such as U-Net and SegNet. The annotated data is used to train these models, which are then used to classify and segment the aerial image into two classes: dashed lines and non-dashed lines. However, the deep learning model may not be able to identify all dashed lines due to poor painting or occlusion by trees or shadows. To address this issue, we proposed a method to add missed lines to the segmentation output. We also extracted the x and y coordinates of each dashed line from the segmentation output, which can be used by city planners to construct a CAD file for digital visualization of the roads.
Permissionless Refereed Tournaments
Venue: arXiv cs.CR 2022
Year: 2022

Scalability problems in programmable blockchains have created a strong demand for secure methods that move the bulk of computation outside the blockchain. One of the preferred solutions to this problem involves off-chain computers that compete interactively to prove to the limited blockchain that theirs is the correct result of a given intensive computation. Each off-chain computer spends effort linear on the cost of the computation, while the blockchain adjudicates disputes spending only logarithmic effort. However, this effort is multiplied by the number of competitors, rendering disputes that involve a significant number of parties impractical and susceptible to Sybil attacks. In this paper, we propose a practical dispute resolution algorithm by which a single honest competitor can win disputes while spending effort linear on the cost of the computation, but only logarithmic on the number of dishonest competitors. This algorithm is a novel, stronger primitive for building permissionless fraud-proof protocols, which doesn't rely on complex economic incentives to be enforced.
Comparison of Three Job Mapping Algorithms for Supercomputer Resource Managers
Venue: arXiv cs.PF 2022
Year: 2022

Performance of supercomputer depends on the quality of resource manager, one of its functions is assignment of jobs to the nodes of clusters or MPP computers. Parts of parallel programs interact with each other with different intensity, and mapping of program to supercomputer nodes influence efficiency of the run. At each program run graph representing application program is to be mapped onto graph of nodes representing a subset of computer system. The both graphs are not known beforehand, hence the mapping must be done in reasonable time while scheduling resources. Three mapping algorithms were explored: parallel versions of simulated annealing, genetic and composite algorithms. A set of experimental runs with different algorithms parameters was performed, comparison of mapping quality and runtime was made, and suggestions on applicability of algorithms for resource managers were provided.